enrichmentFunctional enrichment (GO, KEGG, Pfam, Reactome, …) for a gene set.
| Name | Required | Description | Default |
|---|---|---|---|
| species | No | ||
| identifiers | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of items returned. | |
| items | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "identifiers": [
+ "9606.ENSP00000269305",
+ "9606.ENSP00000005339",
+ "9606.ENSP00000138641"
+ ]
+ },
+ {
+ "identifiers": [
+ "9606.ENSP00000269305"
+ ],
+ "species": 10090
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of items returned.",
+ "type": "integer"
+ },
+ "items": {
+ "items": {
+ "properties": {
+ "category": {
+ "description": "Enrichment category (GO, KEGG, Pfam, Reactome, etc.)",
+ "type": "string"
+ },
+ "fdr": {
+ "description": "False discovery rate",
+ "type": "number"
+ },
+ "inputGeneCount": {
+ "description": "Number of input genes in term",
+ "type": "number"
+ },
+ "ncbiTaxonId": {
+ "description": "NCBI taxonomy ID",
+ "type": "number"
+ },
+ "number_of_genes": {
+ "description": "Number of genes in term",
+ "type": "number"
+ },
+ "number_of_genes_in_background": {
+ "description": "Total genes in background",
+ "type": "number"
+ },
+ "preferredNames": {
+ "description": "Gene names in term (comma-separated)",
+ "type": "string"
+ },
+ "pvalue": {
+ "description": "Statistical p-value",
+ "type": "number"
+ },
+ "term_description": {
+ "description": "Term name or description",
+ "type": "string"
+ },
+ "term_id": {
+ "description": "Database term identifier",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "items",
+ "count"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, open-world, idempotent, and non-destructive behavior, so the description's main contribution is naming the databases (GO, KEGG, Pfam, Reactome) and specifying the input as a gene set. This is useful context but does not disclose additional behavioral nuances.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single compact sentence with no filler, immediately front-loading the core purpose and giving representative database examples. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema and rich annotations cover much of the behavioral surface, so the description need not explain return values. However, the lack of usage guidance and sparse parameter semantics leaves clear gaps for a specialized tool, making it minimally viable but not fully self-sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero schema description coverage, the description must compensate, but it only broadly implies that 'identifiers' form a gene set. It does not explain expected identifier formats (e.g., Ensembl IDs) or the role of the optional 'species' parameter. The schema examples provide some clues, but the description itself adds minimal parameter-level meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as performing functional enrichment (GO, KEGG, Pfam, Reactome) for a gene set. It names the analysis type and target input, distinguishing it from sibling tools like homology or interactions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives, nor any exclusions or prerequisites. The user must infer usage from the tool name and sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route to the same underlying data catalog. The six polymarket_* tools also blur together (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread), and resolve vs resolve_entity is an outright collision an agent will likely misselect.
There is a solid verb_noun core (list_subscriptions, scan_dependency, validate_claim, suggest_questions, compare_entities) but it is mixed with bare nouns (enrichment, homology, interactions, network) and product-prefixed names (pipeworx_feedback, polymarket_edges, ask_pipeworx). No single consistent pattern holds across the set, though the clusters are internally predictable.
At 36 tools this is well above the 25+ threshold for 'too many,' and the sprawl is not justified by a single coherent domain—prediction markets, bioinformatics, brand visibility, npm scanning, and subscription management are jammed together. The count makes the tool surface hard to navigate even with good descriptions.
Within each major cluster the lifecycle feels covered: memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list/recent_alerts), STRING-DB (resolve/homology/interactions/network/enrichment), and Polymarket analysis (scan/edge/arb/fill-risk/track) all form reasonably complete workflows. The main gap is that the server attempts so many domains that none is exhaustively deep, but there are no critical dead ends.